Retrieving application user activities from a memory dump has traditionally been a labor-intensive process, due to both the sheer volume of memory data and the complexity of opaque data structures. We introduce RAM-Weaver, a system that enables contextual querying of user activities from application memory dump files. RAM-Weaver distills essential information from raw dumps and leverages large language models (LLMs) to navigate opaque data structures and support interactive queries. This distillation process can reduce the data volume by up to 99.9% and improve the signal-to-noise ratio by nearly 37 dB, making it feasible to run on smaller, offline models. When combined with full-scale, cloud-hosted models, RAM-Weaver can retrieve user activity data with exceptionally high accuracy.